Currently, autonomous mobile robot is a rapidly developing branch of robotics, and attracts great attention in both research and industry fields. The automatic motion of the autonomous mobile robot is a cyclical automatic process. For each cycle, the autonomous mobile robot first determines which, in the external space, are obstacles and which are free space and then designs a path in the free space. Further, the autonomous mobile robot plans its motion command based on the designed path and completes the motion.
In existing technologies, most obstacle detection methods mainly depend on active sensors such as laser or sonar sensors. However, these detection methods may have characteristics such as low anti-interference ability and high cost. Because visual sensors have characteristics such as small size, low cost and high anti-interference ability, a few detection methods also use visual sensors to detect the obstacles.
In existing visual-based obstacle detection methods for a mobile robot, the mobile robot calculates parallax of two images based on the images obtained by a left camera and a right camera, derives distances of scene objects relative to the robot based on the parallax, and then defines the free space and the obstacles. However, these methods are only limited to the binocular camera. Also, because the parallax of the images needs to be calculated and grid storage needs to be used to store obstacle information, the computation is complex and real-time reaction capability is poor. For methods using a monocular camera, motion compensation is used to detect the obstacles. However, these methods can only be used to detect the obstacles on a road surface, and the scope of its application is only limited to planar motion of the robot, not suitable for space motion.
The disclosed methods and apparatuses are directed to solve one or more problems set forth above and other problems. For example, the disclosed methods and apparatuses can provide technical solutions for detecting obstacles in both a two-dimensional (2D) plane and a three-dimensional (3D) space. The disclosed methods may be applied to any visual-based obstacle detection scene in both the 2D plane and the 3D space.